Piotr Orzechowski (M.Sc.)

Wissenschaftlicher Mitarbeiter

Werdegang

Piotr Orzechowski studied Computer Science at the University of Heidelberg and Karlsruhe Institute of Technology (KIT) with a focus on robotics, automation and cognitive systems. During his master studies he was a co-founder and team leader of KITcar, a student group developing automated model vehicles for the international CaroloCup competition.

In 2016 he wrote his master thesis about "Automated computation of ground truth for target tracking systems using stereoscopic computer vision" at Atlatec GmbH.

Since September 2016 Piotr Orzechowski works as a research scientist towards his PhD, first employed at the Institute of Measurement and Control Systems, now at FZI Research Center for Information Technology.

His research area is safe trajectory and maneuver planning for automated vehicles.

Publikationen

Konferenzbeitrag (3)

The potential of cooperative automated driving to increase traffic flow efficiency and safety has awakened broad interest. Although research in this field is increasing and algorithms are varied, existing literature lacks an accepted taxonomy of cooperative automated driving and cooperative driving systems remain unrated. This paper introduces a novel structure to make cooperative automated driving rateable and further classifies existing works on cooperative behavior. The novel structure and the benefits of different cooperation modes are illustrated by several use cases.

Provable safety is one of the most critical challenges in automated driving. The behavior of numerous traffic participants in a scene cannot be predicted reliably due to complex interdependencies and indiscriminate behavior of humans. Additionally we face high uncertainties and only incomplete environment knowledge. Recent approaches minimize risk with probabilistic and machine learning methods - even under occlusions. These generate comfortable behavior with good traffic flow, but cannot guarantee safety of their maneuvers. Therefore we contribute a safety verification method for trajectories under occlusions. The field-of-view of the ego vehicle and a map are used to identify critical sensing field edges, each representing a potentially hidden obstacle. The state of occluded obstacles is unknown, but can be over-approximated by intervals over all possible states. Then set-based methods are extended to provide occupancy predictions for obstacles with state intervals. The proposed method can verify the safety of given trajectories (e.g. if they ensure collision-free fail-safe maneuver options) w.r.t. arbitrary safe-state formulations. The potential for provably safe trajectory planning is shown in three evaluative scenarios.